Abstract
We introduce a computational method for global optimization of structure and ordering in atomic systems. The method relies on interpolation between chemical elements, which is incorporated in a machine-learning structural fingerprint. The method is based on Bayesian optimization with Gaussian processes and is applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with CO adsorption, and Cu-Ni clusters. The method consistently identifies low-energy structures, which are likely to be the global minima of the energy. For the investigated systems with 23-66 atoms, the number of required energy and force calculations is in the range 3-75.
| Original language | English |
|---|---|
| Article number | 166001 |
| Journal | Physical Review Letters |
| Volume | 127 |
| Issue number | 16 |
| Number of pages | 6 |
| ISSN | 0031-9007 |
| DOIs | |
| Publication status | Published - 2021 |
Bibliographical note
Funding Information:We acknowledge support from the VILLUM Center for Science of Sustainable Fuels and Chemicals, which is funded by the VILLUM Fonden Research Grant (No. 9455).